Impact of Heater Thermal Properties on Nucleate Pool Boiling: Insights from a Multiscale Automata Simulation

This study investigates the influence of heater material properties on nucleate pool boiling using a comprehensive simulation model. Copper and silicon oxide are selected as reference materials due to their properties as excellent and poor heat conductors, respectively. The model integrates well-known heat transfer mechanisms, allowing for the assessment of the effects of these distinct heater materials. The results show that materials with superior thermal diffusivity, such as copper, significantly enhance cooling efficiency during nucleate boiling. Moreover, the study provides insights into the relationship between bubble growth, microlayer recovery beneath a bubble, temperature fluctuations, and heater properties. Comparisons between copper and silicon oxide underscore variations in bubble frequency, attributed to differences in bubble growth time, microlayer recovery time, and material-dependent behavior. The influence of neighboring boiling sites is especially pronounced in silicon oxide due to its low thermal conductivity and diffusivity values. Temperature variations in this material become highly visible due to its very slow response to temperature changes. Simulation results align well with semi-empirical correlations, confirming the model’s success in capturing the intricate phenomena of nucleate pool boiling. In summary, the model reveals that changes in the thermal properties of the heater affect not only boiling performance but also key characteristics of the process, including bubble frequency, boiling patterns, regularity, and cavity reactivation speed.

💡 Research Summary

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The paper presents a multiscale cellular automata model to investigate how the thermal properties of heater materials affect nucleate pool boiling. Two representative substrates—copper (high thermal diffusivity α ≈ 116 mm²/s, conductivity κ ≈ 401 W/m·K) and silicon dioxide (low α ≈ 0.8 mm²/s, κ ≈ 1.4 W/m·K)—are simulated under identical geometric and wetting conditions. The heater is discretized into 16 000 cubic cells (250 µm per side) forming a 1 × 1 cm² plate of 0.25 cm thickness. Heat conduction inside the solid is solved with a modified alternating‑direction implicit (ADI) scheme that permits larger time steps (10⁻³–10⁻⁴ s) while retaining second‑order temporal accuracy.

At the liquid‑solid interface, four heat‑removal mechanisms are modeled: natural convection, micro‑convection induced by bubble departure, combined microlayer and superheated‑liquid evaporation, and radiation. Each mechanism is activated based on the local wetting state and bubble presence. Bubble nucleation sites are randomly placed (20 sites) with radii drawn from a Gaussian distribution (mean 6 µm, σ = 3 µm). A fixed contact angle of 20° governs activation via a thermodynamic criterion that incorporates surface tension, superheat, and the thermal boundary layer thickness. Bubbles grow as spherical caps, coalesce when they meet, and detach once a critical radius is reached; the site then reactivates after microlayer recovery.

Simulation results reveal that the high‑diffusivity copper plate rapidly spreads heat, leading to short bubble growth and microlayer recovery times, higher bubble departure frequency, and relatively smooth surface temperature histories. In contrast, the low‑diffusivity SiO₂ plate exhibits slower heat spreading, longer bubble growth cycles, pronounced temperature spikes, and strong thermal interaction between neighboring bubbles. These differences are quantified through bubble frequency, microlayer recovery time, and temperature fluctuation metrics.

The model’s predictions align closely with established semi‑empirical boiling correlations (e.g., Rohsenow, Cooper), confirming its physical fidelity. Moreover, the automata approach achieves orders‑of‑magnitude reduction in computational cost compared with full direct numerical simulation (DNS), making it feasible to explore a wide range of material properties, surface patterns, and operating conditions.

Limitations include the focus on sub‑critical pool boiling (no post‑departure dynamics), a two‑dimensional cell layout, and a constant contact angle; thus, extensions to three‑dimensional geometries, variable wettability, and near‑critical regimes are suggested for future work.

In summary, the study demonstrates that heater thermal diffusivity and conductivity critically dictate nucleate boiling characteristics such as bubble frequency, microlayer dynamics, and temperature regularity. The presented multiscale automata framework provides an efficient, accurate tool for designing and optimizing heater surfaces in high‑heat‑flux applications across electronics, MEMS, and nuclear reactor technologies.